def init(args):
"""
Load data, build model, create optimizer, create vars to hold metrics, etc.
"""
#need to handle really large text fields
csv.field_size_limit(sys.maxsize)
#load vocab and other lookups
desc_embed = args.lmbda > 0
print("loading lookups...")
dicts = datasets.load_lookups(args, desc_embed=desc_embed)
model = tools.pick_model(args, dicts)
print(model)
if not args.test_model:
optimizer = optim.Adam(model.parameters(),
weight_decay=args.weight_decay,
lr=args.lr)
else:
optimizer = None
params = tools.make_param_dict(args)
return args, model, optimizer, params, dicts
def init(args):
"""
Load data, build model, create optimizer, create vars to hold metrics, etc.
"""
#need to handle really large text fields
csv.field_size_limit(
sys.maxsize) # Sets field size to max available for strings
# freq_params = None
# if args.samples or args.lmbda > 0:
# print("loading code frequencies...")
# code_freqs, n = datasets.load_code_freqs(args.data_path, version=args.version)
# print("code_freqs:", sorted(code_freqs.items(), key=operator.itemgetter(1), reverse=True)[:10], "n:", n)
# freq_params = (code_freqs, n)
#load vocab and other lookups
# desc_embed = args.lmbda > 0
# dicts = datasets.load_lookups(args.data_path, args.vocab, Y=args.Y, desc_embed=desc_embed, version=args.version)
# LOAD VOCAB DICTS
dicts = datasets.load_vocab_dict(
args.vocab_path) # CHANGED args.vocab --> args.vocab_path
model = tools.pick_model(args, dicts)
print(model)
optimizer = optim.Adam(model.params_to_optimize(),
weight_decay=args.weight_decay,
lr=args.lr)
params = tools.make_param_dict(args)
return args, model, optimizer, params, dicts
def init(args):
"""
Load data, build model, create optimizer, create vars to hold metrics, etc.
"""
#need to handle really large text fields
csv.field_size_limit(sys.maxsize)
#load vocab and other lookups
desc_embed = args.lmbda > 0
print("loading lookups...")
dicts = datasets.load_lookups(args, desc_embed=desc_embed)
META_TEST = args.test_model is not None
model, start_epoch, optimizer = tools.pick_model(args, dicts, META_TEST)
print(model)
params = tools.make_param_dict(args)
return args, model, optimizer, params, dicts, start_epoch
def init(args):
"""
Load data, build model, create optimizer, create vars to hold metrics, etc.
"""
#need to handle really large text fields
csv.field_size_limit(
sys.maxsize) # Sets field size to max available for strings
# LOAD VOCAB DICTS
dicts = datasets.load_vocab_dict(
args.vocab_path) # CHANGED args.vocab --> args.vocab_path
model = tools.pick_model(args, dicts)
print(model)
optimizer = optim.Adam(model.params_to_optimize(),
weight_decay=args.weight_decay,
lr=args.lr)
params = tools.make_param_dict(args)
return args, model, optimizer, params, dicts
def init(args):
"""
Load data, build model, create optimizer, create vars to hold metrics, etc.
"""
# need to handle really large text fields
csv.field_size_limit(sys.maxsize)
# load vocab and other lookups
desc_embed = args.lmbda > 0
print("loading lookups...")
dicts = datasets.load_lookups(args, desc_embed=desc_embed)
model = transformer.TransformerAttn(args.Y, args.embed_file, dicts,
args.lmbda, args.gpu, args.embed_size,
args.num_layers, args.heads, args.d_ff,
args.dropout,
args.max_relative_positions)
if args.gpu:
model.cuda()
print(model)
if not args.test_model:
optimizer = optim.Adam(model.parameters(),
weight_decay=args.weight_decay,
lr=args.lr)
optimizer = NoamOpt(
100, 2, 4000,
torch.optim.Adam(model.parameters(),
lr=0,
betas=(0.9, 0.98),
eps=1e-9))
else:
optimizer = None
params = tools.make_param_dict(args)
return args, model, optimizer, params, dicts